Robin: A Novel Method to Produce Robust Interpreters for Deep Learning-Based Code Classifiers

TL;DR

Robin introduces a hybrid approach combining an interpreter and two approximators, leveraging adversarial training and data augmentation to produce robust, high-fidelity explanations for deep learning-based code classifiers, especially under out-of-distribution conditions.

Contribution

Robin is a novel method that creates robust interpreters for deep code classifiers by integrating hybrid structures with adversarial training and data augmentation techniques.

Findings

Robin achieves 6.11% higher fidelity on classifiers.

Robin's interpreters are 67.22% more faithful to approximators.

Robin's approach is 15.87x more robust against out-of-distribution examples.

Abstract

Deep learning has been widely used in source code classification tasks, such as code classification according to their functionalities, code authorship attribution, and vulnerability detection. Unfortunately, the black-box nature of deep learning makes it hard to interpret and understand why a classifier (i.e., classification model) makes a particular prediction on a given example. This lack of interpretability (or explainability) might have hindered their adoption by practitioners because it is not clear when they should or should not trust a classifier's prediction. The lack of interpretability has motivated a number of studies in recent years. However, existing methods are neither robust nor able to cope with out-of-distribution examples. In this paper, we propose a novel method to produce \underline{Rob}ust \underline{in}terpreters for a given deep learning-based code classifier; the method is dubbed Robin. The key idea behind Robin is a novel hybrid structure combining an interpreter and two approximators, while leveraging the ideas of adversarial training and data augmentation. Experimental results show that on average the interpreter produced by Robin achieves a 6.11\% higher fidelity (evaluated on the classifier), 67.22\% higher fidelity (evaluated on the approximator), and 15.87x higher robustness than that of the three existing interpreters we evaluated. Moreover, the interpreter is 47.31\% less affected by out-of-distribution examples than that of LEMNA.